What You Will Do :

- Be a visionary leader providing guidance and mentorship to your team and across the organization on data engineering best practices, data frameworks, and robust ETL/ELT pipelines.

- Be responsible for owning large-scale data platforms and data engineering projects developed by your team in each phase design, implementation, code review, QA, and deployment to production.

- Flex your strength and knowledge on data modeling, data architecture, and high-volume ETL/ELT pipeline development.

- Build a capable team with strong leadership through effective headcount planning, hiring, on-boarding, retention, and continuous improvement.

- Motivate and influence teams to buy into team vision and quarterly roadmap, pushing individuals outside of their comfort zones where necessary.

- Ensure services are built to industry best practices, including observability, architectural patterns, and inter-team dependency mechanisms (like SLOs).

- Work closely with architects, and other teams across the organization to design and implement robust, scalable data solutions and pipelines.

- Collaborate with business leaders to understand core needs when designing data products.

- Champion Data Product principles, Governance, and Discovery across the domain, partnering with other data engineering functions organization-wide.

- Obsessively focus on production readiness for the team including testing, monitoring, deployment, documentation, and proactive troubleshooting.

- Break down complex initiatives into concrete iterative pieces.

- Seek out, define, and evolve best practices within teams area of focus as well as initiatives that raise the standards across the organization.

- Provide visibility into team results and bottlenecks, and drive data-driven action plans.

- Work within an Agile culture to foster continuous improvement at the team and departmental level.

- Proven success in mentoring junior engineers to improve standards and reduce defects in dynamic environments.

We Are a Match Because You Have :

- 10+ years of engineering experience, preferably a mix of start-up and large-company backgrounds.

- eComm domain expertise is a plus.

- Hands-on experience driving software transformations in high-growth, scalable environments.

- Prior experience managing data engineering teams and large-scale data engineering projects.

- Mastery in cross-functional consensus building and influencing without direct authority.

- Experienced in architecting and building large-scale, cloud-based data platforms and ETL/ELT pipelines.

- Successful background designing production systems at scale (fault tolerance, reliability, performance, security).

- Excellent communication skills with demonstrated ability to drive teams and influence results.

- Experience with GCP (target platform) and scale experience with AWS/Azure.

- Expert-level exposure to data frameworks, ETL/ELT pipelines, and data governance principles.

- Experience with high-volume async messaging and large-scale relational/NoSQL data stores.

- Deep understanding of data processing and data pipelines.

- Familiarity with common open source data platforms/tools : Kafka, Spark, Flink, Data Warehouses (Snowflake, BigQuery), Data Lakes, Kubernetes, Java/Python, and NoSQL stores.

- Proven experience with containerization (Docker) and Kubernetes (K8s).

- Experience with large-scale stream/batch processing (e.g. , Kafka, Spark, Flink, Beam).

- Experience working with relational and non-relational databases.

- Experience with event-driven architectures, DDD, and TDD.

- Experience in system monitoring (e.g. , DataDog).

- Familiarity with challenges in productionizing and scaling ML systems.

- Strong foundation in computer science, distributed systems, and data structures.

- Experience leveraging modern IDEs and AI-assisted development tools (e.g. , Cursor, GitHub Copilot) to accelerate cycles.